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Simultaneous Interrogation of Cancer Omics to Identify Subtypes With Significant Clinical Differences
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Catalogue of Somatic Mutations in Cancer (COSMIC)
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Molecular profiling of the “plexinome” in melanoma and pancreatic cancer
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Epigenetic inactivation of microRNA gene hsa‐mir‐9‐1 in human breast cancer
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iTRAQ analysis of colorectal cancer cell lines suggests Drebrin (DBN1) is overexpressed during liver metastasis
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Predictive value of APE1, BRCA1, ERCC1 and TUBB3 expression in patients with advanced non-small cell lung cancer (NSCLC) receiving first-line platinum–paclitaxel chemotherapy
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Akt, FoxO and regulation of apoptosis
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Drebrin: A new oncofetal biomarker associated with prognosis of lung adenocarcinoma
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The role of drebrin in glioma migration and invasion
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moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets
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Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
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AFX-like Forkhead transcription factors mediate cell-cycle regulation by Ras and PKB through p27kip1
inferredcites →https://doi.org/10.1038/75556openalex-snapshot
Gene Ontology: tool for the unification of biology
inferredcites →https://doi.org/10.1038/nature08987openalex-snapshot
International network of cancer genome projects
inferredcites →https://doi.org/10.1038/nature21386openalex-snapshot
Integrated genomic and molecular characterization of cervical cancer
inferredcites →https://doi.org/10.1038/ncb2024openalex-snapshot
miR-9, a MYC/MYCN-activated microRNA, regulates E-cadherin and cancer metastasis
inferredcites →https://doi.org/10.1038/ng.2764openalex-snapshot
The Cancer Genome Atlas Pan-Cancer analysis project
inferredcites →https://doi.org/10.1038/nmeth.2810openalex-snapshot
Similarity network fusion for aggregating data types on a genomic scale
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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
inferredcites →https://doi.org/10.1038/nrc3721openalex-snapshot
Principles and methods of integrative genomic analyses in cancer
inferredcites →https://doi.org/10.1038/onc.2010.305openalex-snapshot
Hsa-miR-9 methylation status is associated with cancer development and metastatic recurrence in patients with clear cell renal cell carcinoma
inferredcites →https://doi.org/10.1073/pnas.1208949110openalex-snapshot
Pattern discovery and cancer gene identification in integrated cancer genomic data
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The Forkhead Transcription Factor FOXO4 Induces the Down-regulation of Hypoxia-inducible Factor 1α by a von Hippel-Lindau Protein-independent Mechanism
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Missing value estimation methods for DNA microarrays
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Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data
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New approach reveals CD28 and IFNG gene interaction in the susceptibility to cervical cancer
inferredcites →https://doi.org/10.1093/nar/gkn923openalex-snapshot
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists
inferredcites →https://doi.org/10.1093/nar/gkq929openalex-snapshot
COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer
inferredcites →https://doi.org/10.1093/nar/gkr988openalex-snapshot
KEGG for integration and interpretation of large-scale molecular data sets
inferredcites →https://doi.org/10.1093/nar/gks1193openalex-snapshot
NCBI GEO: archive for functional genomics data sets—update
inferredcites →https://doi.org/10.1093/nar/gks725openalex-snapshot
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
inferredcites →https://doi.org/10.1093/nar/gkx1067openalex-snapshot
miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions
inferredcites →https://doi.org/10.1093/nar/gky889openalex-snapshot
Multi-omic and multi-view clustering algorithms: review and cancer benchmark
inferredcites →https://doi.org/10.1096/fj.04-2727fjeopenalex-snapshot
FOXO‐dependent expression of the proapoptotic protein Bim: pivotal role for apoptosis signaling in endothelial progenitor cells
inferredcites →https://doi.org/10.1101/gr.093310.109openalex-snapshot
The presence of RNA polymerase II, active or stalled, predicts epigenetic fate of promoter CpG islands
inferredcites →https://doi.org/10.1109/tbme.2016.2573285openalex-snapshot
–Omic and Electronic Health Record Big Data Analytics for Precision Medicine
inferredcites →https://doi.org/10.1111/cas.12880openalex-snapshot
Novel combination of serum microRNA for detecting breast cancer in the early stage
inferredcites →https://doi.org/10.1111/j.1750-3639.2008.00184.xopenalex-snapshot
MiR‐92b and miR‐9/9* Are Specifically Expressed in Brain Primary Tumors and Can Be Used to Differentiate Primary from Metastatic Brain Tumors
inferredcites →https://doi.org/10.1126/science.1160809openalex-snapshot
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
inferredcites →https://doi.org/10.1145/1835804.1835848openalex-snapshot
Unsupervised feature selection for multi-cluster data
inferredcites →https://doi.org/10.1158/0008-5472.can-09-4456openalex-snapshot
Oncogenic Role of miR-483-3p at the IGF2/483 Locus
inferredcites →https://doi.org/10.1158/0008-5472.can-14-2718openalex-snapshot
Aberrant Expression of proPTPRN2 in Cancer Cells Confers Resistance to Apoptosis
inferredcites →https://doi.org/10.1158/1055-9965.epi-14-1228openalex-snapshot
Racial Variation in Breast Tumor Promoter Methylation in the Carolina Breast Cancer Study
inferredcites →https://doi.org/10.1158/1078-0432.ccr-09-1587openalex-snapshot
miR-195 and miR-483-5p Identified as Predictors of Poor Prognosis in Adrenocortical Cancer
inferredcites →https://doi.org/10.1158/1078-0432.ccr-10-1803openalex-snapshot
Prediction of Postoperative Recurrence-Free Survival in Non–Small Cell Lung Cancer by Using an Internationally Validated Gene Expression Model
inferredcites →https://doi.org/10.1186/1471-2105-10-165openalex-snapshot
A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data
inferredcites →https://doi.org/10.1186/1471-2407-13-327openalex-snapshot
Activation of AMPK inhibits cervical cancer cell growth through AKT/FOXO3a/FOXM1 signaling cascade
inferredcites →https://doi.org/10.1186/1756-9966-28-82openalex-snapshot
Down-regulated miR-9 and miR-433 in human gastric carcinoma
inferredcites →https://doi.org/10.1186/1756-9966-30-87openalex-snapshot
Apoptosis in cancer: from pathogenesis to treatment
inferredcites →https://doi.org/10.1186/1756-9966-33-9openalex-snapshot
Identification of intrinsic subtype-specific prognostic microRNAs in primary glioblastoma
inferredcites →https://doi.org/10.1371/journal.pcbi.1005781openalex-snapshot
Clusternomics: Integrative context-dependent clustering for heterogeneous datasets
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A Tissue Biomarker Panel Predicting Systemic Progression after PSA Recurrence Post-Definitive Prostate Cancer Therapy
inferredcites →https://doi.org/10.1371/journal.pone.0085150openalex-snapshot
Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures
inferredcites →https://doi.org/10.18632/oncotarget.3424openalex-snapshot
A novel role for drebrin in regulating progranulin bioactivity in bladder cancer
inferredcites →https://doi.org/10.2147/hp.s93413openalex-snapshot
The role of hypoxia in cancer progression, angiogenesis, metastasis, and resistance to therapy
inferredcites →https://doi.org/10.3389/fgene.2017.00084openalex-snapshot
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
inferredcites →https://doi.org/10.4161/epi.29628openalex-snapshot
A novel cancer-germline transcript carrying pro-metastatic miR-105 andTET-targeting miR-767 induced by DNA hypomethylation in tumors
inferredcites →https://doi.org/10.5301/jbm.5000161openalex-snapshot
Expression of ERCC1 and TUBB3 in Locally Advanced Cervical Squamous Cell Cancer and its Correlation with Different Therapeutic Regimens
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Integrative Identification of Driver Genes for Enhanced Personalized Cancer Subtype Classification
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Multi-omics Data and Analytics Integration in Ovarian Cancer
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Plant miRNA–lncRNA Interaction Prediction with the Ensemble of CNN and IndRNN
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Learning vector quantized representation for cancer subtypes identification
inferred← citeshttps://doi.org/10.1016/j.csbj.2024.04.015openalex-snapshot
Advanced computational predictive models of miRNA-mRNA interaction efficiency
inferred← citeshttps://doi.org/10.1016/j.ins.2024.120305openalex-snapshot
Accurate multi-view clustering to seek the cross-viewed yet uniform sample assignment via tensor feature matching
inferred← citeshttps://doi.org/10.1016/j.ins.2025.123044openalex-snapshot
Many-objective island-based evolutionary algorithm for biomarker selection and cancer molecular diagnosis in high-dimensional omics data
inferred← citeshttps://doi.org/10.1016/j.neucom.2019.10.014openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neucom.2020.07.060openalex-snapshot
Exsavi: Excavating both sample-wise and view-wise relationships to boost multi-view subspace clustering
inferred← citeshttps://doi.org/10.1016/j.omtn.2019.11.014openalex-snapshot
Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?
inferred← citeshttps://doi.org/10.1016/j.ymeth.2020.07.008openalex-snapshot
Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer
inferred← citeshttps://doi.org/10.1038/s41416-024-02587-wopenalex-snapshot
wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning
inferred← citeshttps://doi.org/10.1093/bib/bbaa102openalex-snapshot
Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets
inferred← citeshttps://doi.org/10.1093/bib/bbac155openalex-snapshot
RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation
inferred← citeshttps://doi.org/10.1093/bib/bbac488openalex-snapshot
Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning
inferred← citeshttps://doi.org/10.1093/bib/bbz133openalex-snapshot
MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa074openalex-snapshot
PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA–lncRNA interaction prediction
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab535openalex-snapshot
Cancer subtype identification by consensus guided graph autoencoders
inferred← citeshttps://doi.org/10.1109/access.2020.2972283openalex-snapshot
Prioritizing Human Microbe-Disease Associations Utilizing a Node-Information-Based Link Propagation Method
inferred← citeshttps://doi.org/10.1109/bibm52615.2021.9669348openalex-snapshot
Multi-Omics Data Clustering via the Guidance of Highly Correlated Features
inferred← citeshttps://doi.org/10.1109/bibm52615.2021.9669387openalex-snapshot
By hybrid neural networks for prediction and interpretation of transcription factor binding sites based on multi-omics
inferred← citeshttps://doi.org/10.1109/tcbb.2021.3106344openalex-snapshot
Identifying Cancer Patient Subgroups by Finding Co-Modules From the Driver Mutation Profiles and Downstream Gene Expression Profiles
inferred← citeshttps://doi.org/10.1109/tcbb.2021.3123910openalex-snapshot
COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures
inferred← citeshttps://doi.org/10.1109/tcbb.2021.3139597openalex-snapshot
Similarity Fusion via Exploiting High Order Proximity for Cancer Subtyping
inferred← citeshttps://doi.org/10.1109/tcbb.2022.3176859openalex-snapshot
Layer-Specific Modules Detection in Cancer Multi-Layer Networks
inferred← citeshttps://doi.org/10.1109/tcbb.2022.3203185openalex-snapshot
A Clustering Method Unifying Cell-Type Recognition and Subtype Identification for Tumor Heterogeneity Analysis
inferred← citeshttps://doi.org/10.1109/tcyb.2020.3026396openalex-snapshot
Fast and Accurate Clustering of Multiple Modality Data via Feature Matching
inferred← citeshttps://doi.org/10.1109/tcyb.2021.3086153openalex-snapshot
Deep Multiview Clustering via Iteratively Self-Supervised Universal and Specific Space Learning
inferred← citeshttps://doi.org/10.1109/tmm.2024.3385986openalex-snapshot
Unsupervised Dual Hashing Coding (UDC) on Semantic Tagging and Sample Content for Cross-Modal Retrieval
inferred← citeshttps://doi.org/10.1145/3386052.3386063openalex-snapshot
Breast Cancer Subtype by Imbalanced Omics Data through A Deep Learning Fusion Model
inferred← citeshttps://doi.org/10.1186/s12859-021-04253-xopenalex-snapshot
IOAT: an interactive tool for statistical analysis of omics data and clinical data
inferred← citeshttps://doi.org/10.1186/s12859-025-06318-7openalex-snapshot
DriverSub-SVM: a machine learning approach for cancer subtype classification by integrating patient-specific and global driver genes
inferred← citeshttps://doi.org/10.1186/s12911-022-01938-yopenalex-snapshot
Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold
inferred← citeshttps://doi.org/10.12677/aam.2023.1212512openalex-snapshot
Cancer Subtyping Algorithm Using Multi-Omics Data and Convolutional Autoencoders
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1009224openalex-snapshot
Evaluation and comparison of multi-omics data integration methods for cancer subtyping
inferred← citeshttps://doi.org/10.1371/journal.pone.0232578openalex-snapshot
Prediction of miRNA targets by learning from interaction sequences
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2428367/v1openalex-snapshot
MMKNF: Integrating multi-omics data to identify cancer subtypes based on multi-kernel network fusion
inferred← citeshttps://doi.org/10.2174/0115748936360644250127095005openalex-snapshot
Integrative Multi-Omics Approaches for Personalized Medicine and Health
inferred← citeshttps://doi.org/10.2196/71906openalex-snapshot
Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review
inferred← citeshttps://doi.org/10.29328/journal.jro.1001035openalex-snapshot
Imaging modalities delivery of RNAi therapeutics in cancer therapy and clinical applications
inferred← citeshttps://doi.org/10.3389/fbioe.2020.00268openalex-snapshot
Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data
inferred← citeshttps://doi.org/10.3389/fgene.2019.01054openalex-snapshot
Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis
inferred← citeshttps://doi.org/10.3389/fgene.2019.01353openalex-snapshot
Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function
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inferred← citeshttps://doi.org/10.3389/fgene.2022.806842openalex-snapshot
MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis
inferred← citeshttps://doi.org/10.3389/fgene.2022.855629openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fgene.2022.884028openalex-snapshot
Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
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Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers
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Comparative Analysis of Deep Learning Algorithm for Cancer Classification using Multi-omics Feature Selection
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Additional file 1 of Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold
inferred← citeshttps://doi.org/10.7287/peerj.10813v0.1/reviews/1openalex-snapshot
Peer Review #1 of "6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.10813v0.1/reviews/2openalex-snapshot
Peer Review #2 of "6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.10813v0.1/reviews/3openalex-snapshot
Peer Review #3 of "6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.10813v0.2/reviews/1openalex-snapshot
Peer Review #1 of "6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj.10813v0.2/reviews/2openalex-snapshot
Peer Review #2 of "6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj.10813v0.2/reviews/3openalex-snapshot
Peer Review #3 of "6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj.10813openalex-snapshot
6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning
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